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Targeted Learning: Causal Inference for Observational and Experimental Data
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Citations
Cited by
Recursive partitioning for heterogeneous causal effects
Athey, S., & Imbens, G. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1510489113 · Cited by 1,586
Principles of confounder selection
VanderWeele, T. J. (2019) · European Journal of Epidemiology · doi:10.1007/s10654-019-00494-6 · Cited by 1,535
Per-Protocol Analyses of Pragmatic Trials
Hernán, M. A., & Robins, J. M. (2017) · New England Journal of Medicine · doi:10.1056/nejmsm1605385 · Cited by 660
The seven tools of causal inference, with reflections on machine learning
Pearl, J. (2019) · Communications of the ACM · doi:10.1145/3241036 · Cited by 659
Ethical Machine Learning in Healthcare
Chen, I. Y., Pierson, E., Rose, S., Joshi, S., Ferryman, K., & Ghassemi, M. (2021) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-092820-114757 · Cited by 645
Self-citation
Metadata
- Authors
- van der Laan, M. J., & Rose, S.
- Venue
- Springer Series in Statistics
- Publisher
- Springer New York
- Published
- 2011
- ISSN
- 0172-7397 (ISSN-L)
- OpenAlex
- W4248240383
- Cited by
- 864
- References
- 0
- FWCI
- 4.49
- Type
- book
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Targeted Learning: Causal Inference for Observational and Experimental Data. https://app.cochat.ai/papers/10.1007/978-1-4419-9782-1
- The paper
- van der Laan, M. J., & Rose, S. (2011). Targeted Learning: Causal Inference for Observational and Experimental Data. Springer Series in Statistics. https://doi.org/10.1007/978-1-4419-9782-1
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
